Merge pull request #39 from CostaLab/devel

Fixed vignettes

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: sigurd
 Type: Package
 Title: Single cell Genotyping Using RNA Data
-Version: 0.2.61
+Version: 0.3.0
 Authors@R: c(
   person(given = "Martin",
          family = "Grasshoff",

README.md

@@ -33,7 +33,7 @@ The mutation data was obtained from the Sanger Institute Catalogue Of Somatic Mu
 
 ```
 
-# Current Features v0.2.61
+# Current Features v0.3.0
 
 - Loading data from VarTrix and MAEGATK.
 - Transforming the data to be compatible for joint analysis.

vignettes/BoneMarrow_SIGURD.Rmd

@@ -0,0 +1,78 @@
+---
+title: "Analysis of Blastic Plasmacytoid Dendritic Cell Neoplasm (BPDCN)"
+author: Martin Grasshoff<sup>1</sup>, Ivan G. Costa<sup>1</sup>
+affiliations: 1. Institute for Computational Genomics, Faculty of Medicine, RWTH Aachen University, Aachen, 52074 Germany
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Analysis of Blastic Plasmacytoid Dendritic Cell Neoplasm (BPDCN)}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+header-includes:
+  - \usepackage[utf8]{inputenc}
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, dev = "CairoPNG")
+```
+
+## Analysis of Blastic Plasmacytoid Dendritic Cell Neoplasm (BPDCN)
+
+Here, we compare the analysis of cells from a patient with BPDCN.
+
+The original script is available here: https://github.com/petervangalen/MAESTER-2021/blob/main/4_CH_sample/4.2_Variant_Selection.R
+
+In this setup, the cells are all from a single donor and do not have to be separated into categories. 
+
+Then small clones that might characterize the clonal lineages in the sample are selected.
+
+## Loading necessary packages.
+
+We load the data from MAESTER. 
+
+```{r Packages, warning = FALSE}
+print("Libraries for SIGURD.")
+suppressPackageStartupMessages(library(sigurd))
+suppressPackageStartupMessages(library(SummarizedExperiment))
+suppressPackageStartupMessages(library(ggplot2))
+```
+
+## Loading the data using SIGURD.
+
+```{r Loading_SIGURD, warning = FALSE}
+# The design matrix contains information for the genotyping data.
+genotyping <- LoadingMAEGATK_typewise(patient = "BPDCN712", samples_file = "data/MAESTER_Reproduction.csv", type_use = "Amplicon_MT", verbose = FALSE)
+
+# Loading the scRNA-seq data.
+scrna <- readRDS("/data/MPN/exp/scRNA/MPN_mutations/SIGURD_paper/sigurd/data/BPDCN712_Seurat_with_TCR_Renamed.rds")
+```
+
+## Generating a block list
+
+Variants that are also detected in the cell mixture data is treated as possible false positives. They are used as a blacklist and are removed from the results.
+
+```{r warning = FALSE}
+# Loading the TenX Cell Mixture Genotyping.
+
+genotyping_tenx <- load_object("/data/MPN/exp/scRNA/MPN_mutations/SIGURD_paper/sigurd/data/TenX_CellMixture_Genotyping.rds.lz4")
+blocklist <- AllelFrequencyFoldChange(genotyping_tenx, group_of_interest = "CellType", group1 = "K562", group2 = "BT142", maximum_foldchange = 5, minimum_coverage = 5, minimum_allele_freq = 0.001, maximum_allele_freq = 0.999)$Variant
+
+# We add the variant chrM_1583_A_G by hand. It is identified as misleading based on downstream analysis.
+blocklist <- c(blocklist, "chrM_1583_A_G")
+```
+## Selecting the Variants of Interest
+
+Now, we select the variants of interest from the loaded data.
+
+```{r warning = FALSE}
+voi.ch.sigurd <- VariantSelection_TopCells(genotyping, min_coverage = 5, quantiles = 0.9, thresholds = 0, top_cells = 10, top_VAF = 0.5, min_quality = 30, remove_nocall = FALSE, verbose = FALSE)
+voi.ch.sigurd <- voi.ch.sigurd[!voi.ch.sigurd %in% blocklist]
+print(voi.ch.sigurd)
+```
+
+## Visualisation using SIGURD
+
+```{r Vis_SIGURD, fig.width = 6, fig.height = 4, warning = FALSE}
+genotyping <- Filtering(SE = genotyping, cells_include = colnames(scrna))
+colData(genotyping)$CellType <- scrna$CellType
+HeatmapVoi(SE = genotyping, voi = voi.ch.sigurd, annotation_trait = "CellType", sort_cells = TRUE, remove_empty_cells = TRUE, minimum_allele_freq = 0.01)
+```